@parallel vs. native loops in julia

Question:
I run some example and I got some result. I got for the large number of iteration we can get a good result but for less amount of iteration we can get a worse result.

I know there is a little overhead and it’s absolutely ok, but is there any way to run some loop with less amount of iteration in parallel way better than sequential way?
x = 0
@time for i=1:200000000
x = Int(rand(Bool)) + x
end

Answer:
Q: is there any way to run some loop with less amount of iteration in parallel way better than sequential way?

A: Yes.

1) Acquire more resources ( processors to compute, memory to store ) if all this ought get sense

2) Arrange the workflow smarter – to benefit from register-based code, from harnessing the cache-lines’s sizes upon each first fetch, deploy re-use where possible ( hard work? yes, it is hard work, but why to repetitively pay 150+ [ns] instead of having paid this once and reuse well-aligned neighbouring cells just within ~ 30 [ns] latency-costs ( if NUMA permits )? ). Smarter workflow also often means code re-designs with respect to increasing the resulting assembly-code "density"-of-computations and tweaking the code so as to better by-pass the ( optimising-)-superscalar processor hardware design tricks, which are of no use / positive-benefit in highly-tuned HPC computing payloads.